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Journal of Computer and System Sciences
Article . 2023 . Peer-reviewed
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Article . 2023
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https://dx.doi.org/10.48550/ar...
Article . 2016
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Article . 2023
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Markov chains and unambiguous automata

Authors: Christel Baier; Stefan Kiefer; Joachim Klein 0001; David Müller 0001; James Worrell 0001;

Markov chains and unambiguous automata

Abstract

Unambiguous automata are nondeterministic automata in which every word has at most one accepting run. In this paper we give a polynomial-time algorithm for model checking discrete-time Markov chains against ω-regular specifications represented as unambiguous automata. We furthermore show that the complexity of this model checking problem lies in NC: the subclass of P comprising those problems solvable in poly-logarithmic parallel time. These complexity bounds match the known bounds for model checking Markov chains against specifications given as deterministic automata, notwithstanding the fact that unambiguous automata can be exponentially more succinct than deterministic automata. We report on an implementation of our procedure, including an experiment in which the implementation is used to model check LTL formulas on Markov chains.

38 pages, accepted at JCSS. The first version (v1), 50 pages, is the full version of a paper accepted at CAV 2016

Country
United Kingdom
Related Organizations
Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Specification and verification (program logics, model checking, etc.), Markov chains, Formal Languages and Automata Theory (cs.FL), D.2.4, Computer Science - Formal Languages and Automata Theory, Formal languages and automata, Markov chains (discrete-time Markov processes on discrete state spaces), model checking, Logic in Computer Science (cs.LO), D.2.4; F.3.1, F.3.1, unambiguous automata, probabilistic verification

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Top 10%
Top 10%
Green
hybrid